Performance Evaluation of Heavy-Duty Vehicles Equipped with Automatic Transmissions and Powertrain Adaptive Systems in Forestry Transportation
Bibliographic record
Abstract
The purpose of this study was to evaluate automatic transmissions in a forestry context by comparing their performance with that of standard manual transmissions, and assessing the possibility of improving fuel efficiency by adapting the engine and automatic transmission performances to the vehicle's load. Long-haul test results showed that during the test day, the degradation in driver performance with the manual transmission truck translated into a 2.9% relative increase in fuel consumption when compared with the automatic transmission truck. The fleet data assessment indicated no obvious difference in fuel consumption between the performance of automatic transmissions and manual transmissions. One system for adapting engine performance to vehicle load uses an onboard weigh scale to determine the load status of the vehicle. Long-haul test results for the engine adaptive system showed a 10.6% improvement when compared with the manual transmission control truck, and 7.9% when compared with the automatic control truck. Computer simulations of the long-haul test showed similar results. In the second system, an electronic control triggers the automatic transmission to convert to an economy shift pattern when the truck is empty. The operational fuel data indicated an overall 4.6% fuel improvement, calculated using baseline and test line ratios between the test and control truck. The results of this study indicate that automatic transmissions can eliminate driver shifting ability as a factor in fuel efficiency and offer the possibility of adapting a vehicle's performance to meet the changing demands placed on the engine during a trip.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".